Activation of signaling pathways regulating translation initiation in human skeletal muscle with feeding and resistance exercise
Bibliographic record
Abstract
The regulation of the muscle mass is acutely controlled by the processes of muscle protein synthesis and breakdown. Feeding and resistance exercise stimulate muscle protein synthesis independently and synergistically. Our aim was to investigate the activation of important signaling proteins involved in the regulation of translation initiation in humans in four conditions: fasted, fed, rested and post-resistance exercise. Seven males completed two heavy unilateral resistance leg exercise trials, with their contralateral leg acting as non-exercised (rested) comparator, 1wk apart. One trial was in the fasted condition while the other was in the fed state – consumption of a mixed-meal drink (1000kJ, 10g protein) q90min. Biopsies were taken from both legs 6h post-exercise. Exercise increased Akt, P70s6k, and rps6 phosphorylation (P<0.05). Feeding increased phosphorylation of Focal adhesion kinase (FAK; P<0.05). Phosphorylation of GSK3β tended to increase with exercise and feeding (P=0.068). There was no significant change in mTOR phosphorylation status. Heavy resistance exercise increases the active phosphorylated forms of key members of the Akt pathway, while feeding appears to affect FAK activation. There was a tendency for feeding and exercise to increase GSK3β phosphorylation, which may reduce its inhibitory effect on translation initiation. Supported by NSERC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".